The student resilience survey: Psychometric validation and associations with mental health

Policies, designed to promote resilience, and research, to understand the determinants and correlates of resilience, require reliable and valid measures to ensure data quality.

RESEARCH ARTICLE

The student resilience survey:

psychometric validation and associations

with mental health

Suzet Tanya Lereya1, Neil Humphrey2, Praveetha Patalay3, Miranda Wolpert1*, Jan R Böhnke4, Amy Macdougall5

and Jessica Deighton1

Abstract

Background: Policies, designed to promote resilience, and research, to understand the determinants and correlates

of resilience, require reliable and valid measures to ensure data quality The student resilience survey (SRS) covers a range of external supports and internal characteristics which can potentially be viewed as protective factors and can

be crucial in exploring the mechanisms between protective factors and risk factors, and to design intervention and prevention strategies This study examines the validity of the SRS

Methods: 7663 children (aged 11–15 years) from 12 local areas across England completed the SRS, and

question-naires regarding mental and physical health Psychometric properties of 10 subscales of the SRS (family connection, school connection, community connection, participation in home and school life, participation in community life, peer support, self-esteem, empathy, problem solving, and goals and aspirations) were investigated by confirmatory

factor analysis (CFA), differential item functioning (DIF), differential test functioning (DTF), Cronbach’s α and McDon-ald’s ω The associations between the SRS scales, mental and physical health outcomes were examined.

Results: The results supported the construct validity of the 10 factors of the scale and provided evidence for

accepta-ble reliability of all the subscales Our DIF analysis indicated differences between boys and girls, between primary and secondary school children, between children with or without special educational needs (SEN) and between children with or without English as an additional language (EAL) in terms of how they answered the peer support subscale of the SRS Analyses did not indicate any DIF based on free school meals (FSM) eligibility All subscales, except the peer support subscale, showed small DTF whereas the peer support subscale showed moderate DTF Correlations showed that all the student resilience subscales were negatively associated with mental health difficulties, global subjective distress and impact on health Random effects linear regression models showed that family connection, self-esteem, problem solving and peer support were negatively associated with all the mental health outcomes

Conclusions: The findings suggest that the SRS is a valid measure assessing these relevant protective factors, thereby

serving as a valuable tool in resilience and mental health research

Keywords: Resilience, School surveys, Mental health, Quality of life, Psychometrics

© The Author(s) 2016 This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/ publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated.

Background

Over the past two decades, there has been a

substan-tial increase in resilience research [1 2], following

dissatisfaction with ‘deficit’ models of illness and psycho-pathology [3] Resilience is defined as the maintenance

of positive adjustment in the context of exposure to sig-nificant adversity [4] Key protective factors that confer resilience include positive individual characteristics, functional family relationships and a supportive environ-ment outside the family [5 6] Individual characteristics such as self-control, empathy, intelligence, self-esteem

Open Access

*Correspondence: Ebpu@annafreud.org

Centre for Children and Families, London N1 9JH, UK

Full list of author information is available at the end of the article

and problem-solving skills have been identified as

benefi-cial whether someone is facing low or high adversity [e.g

7] Similarly, warm relationships within the family and

well-structured home environments are important for

positive development in all children even in the absence

of exposure to stressful life events However, having a

supportive family has been shown to be particularly

important for children trying to cope with stressful

expe-riences [e.g 8] Lastly, supportive environments outside

the family such as availability of social support, school

connectedness, having good neighbours and positive role

models have been identified as potential protective

fac-tors [e.g 5]

While resilience is conceived of as an end product

of buffering processes that do not eliminate risks and

stress but allow the individual to deal with them

effec-tively, protective factors have been viewed as

mod-erators of risk and adversity that enhance positive (i.e

developmentally appropriate) outcomes [4 9] The

measurement of a range of factors that promote positive

outcomes is crucial to explore the mechanisms between

protective factors and risk factors, and to design

inter-vention and preinter-vention strategies The student resilience

survey [SRS; 10] covers a range of external supports and

internal characteristics which can potentially be viewed

as protective factors It was constructed by combining

elements from two surveys: the California Healthy Kids

Survey [11] and the Perceptions of Peer Support Scale

[12], assessing student perceptions of their individual

characteristics, protective resources from family, peer,

school and community

The initial SRS development study by Sun and

Stew-art has supported the validity of the scale [10] However,

there were several limitations to the SRS validation

Firstly, the samples of children were drawn from only

20 primary schools in the state of Queensland,

Aus-tralia Sampling from a larger selection of schools, and

a wider geographical area, is clearly needed for a more

robust assessment of the psychometric properties of this

measure Secondly, although the initial validation study

comprised a large sample size (n = 2794), this study will

further provide confirmation by including reports from

over 7000 children Thirdly, SRS has only been validated

using a scale-level approach Scale-level analysis does

not account for how individuals at different levels of

the latent construct perform on the individual items of

an instrument [13] Item-level approaches allow

exami-nation of how individual subject responses on items of

an instrument relate to an unobservable trait [13]

Dif-ferential item functioning [DIF; 14] allows for

investigat-ing item response probability based on different groups

DIF is present when individuals from different

sociode-mographic groupings (such as gender or ethnicity) have

a different probability of answering an item [15] Lastly, the initial validation did not investigate the association between SRS subscales and mental health outcomes It

is expected that most of the SRS subscale scores will be negatively correlated with emotional and behavioural problems [e.g 16, 17] and attainment of good health [18]

The SRS can be an important tool in assessing the impact of protective factors when investigating the rela-tionship between risk and psychological outcome and development The purposes of the present study were threefold First, we aimed to replicate the psychometric characteristics of the SRS found in its initial investiga-tion, this time with an English sample [10] Second, we aimed to investigate the measurement invariance in regard to several subgroups Third, we aimed to assess the relationships between the SRS domains and children’s mental health outcomes

Methods

Sample

Data were collected in 2015 from children who were part

of a large project that focused on the promotion of resil-ience and emotional wellbeing (‘HeadStart’, funded by the Big Lottery Fund) in 12 local areas across 90 schools, England The analyses reported are based on surveys completed by 7663 pupils (42.3% male); 1967 pupils were

in primary school (year 6, mean age = 11.38, SD = 0.29) and 5696 pupils were in secondary school (years 7, 8 and 9, mean age = 13.31, SD = 0.86) For the item-level DIF analysis all items needed to be complete, hence only pupils who completed all items were included (sample size ranged from 6047 to 6123)

The sample was not drawn to be representative of all school children in England; it was based on local areas that were part of the HeadStart programme and each of the 12 local areas selected the schools to participate [19] Overall, 5496 (72.8%) of pupils were White British (com-pared to the national average of 76.2%), and 6176 (81.6%) pupils’ first language was English (compared to the national average of 82.5%) 1452 (19.1%) were eligible for free school meals (compared to the national average of 16.2%—including nursery schools), 131 (1.7%) had a statement of special educational needs1 (compared to the national average of 2.8%) and a further 1159 (15.1%) had any elevated special educational needs, albeit not great enough to meet the threshold for a full statement

of SEN

the nature of a given child’s needs that is produced following a process of statutory assessment (by, for example, an educational psychologist).

Student resilience survey (SRS)

The SRS is a 47-item measure comprising 12 subscales

measuring students’ perceptions of their individual

char-acteristics as well as protective factors embedded in the

environment Frequency of each item was rated on a

5-point scale (1 = never to 5 = always)

As this was part of a larger project and it was

impor-tant not to burden the pupils with a long survey, only

10 of the SRS subscales (out of 12) were selected to be

included into the survey with other validated

meas-ures The 10 chosen subscales were: family connection,

school connection, community connection,

participa-tion in home and school life, participaparticipa-tion in community

life, peer support, self-esteem, empathy, problem

solv-ing, and goals and aspirations As the main project was

interested in identifying protective factors in a child’s

life, the pro-social peers (two items: my friends try to do

what is right; my friends do well in school) and

commu-nication and cooperation (three items: I help other

peo-ple; I enjoy working with other students; I stand up for

myself) subscales were not included Moreover, the scale

was adapted to English school children based on

discus-sions with young advisors from Common Room (a young

people’s advocacy and engagement group with a specific

focus on disability, health and mental health) Four items

were edited so that they were more general and

suit-able for school-aged children in England (i.e instead of

“are there students at your school who would ask you to

play when you are all alone”, it has been changed to “are

there students at your school who would ask you to join

in when you are all alone”; instead of “are there students

at your school who would help you if you hurt yourself

in the playground”, it has been changed to “are there

students at your school who would help you if you hurt

yourself”; instead of “are there students at your school

who would invite you to play at their home”, it has been

changed to “are there students at your school who would

invite you to their home”; instead of “are there students

at your school who would share things like stickers, toys

& games with you”, it has been changed to are there

stu-dents at your school who would share things with you”)

Lastly, one item from the peer support scale (tell you

you’re good at things) was omitted

Mental health difficulties were measured with the me

and my feelings questionnaire (formerly known as Me

and My School measure, M&MS) It is a 16-item

meas-ure comprising a 10-item emotional difficulties scale and

a 6-item behavioural difficulties scale [20, 21] Each item

includes a short statement (e.g I am lonely; I get angry)

measured on a 3-point Likert scale (0 = never,

1 = some-times, and 2  =  always) (emotional problems sum score

mean = 5.17, SD = 3.87; behavioural problems sum score

mean = 3.05, SD = 2.52) Cronbach’s αs in the current

sample were 0.84 for emotional problems (n = 7187) and 0.80 for behavioural problems (n = 7243)

Global subjective distress was measured with child

out-come rating scale (CORS) CORS consists of four items: how am I doing; how are things in my family; how am I doing at school; and how is everything going The rating scale is a 10 cm line with a happy face at one end and a sad face at the other; children are asked to put a mark on the line to indicate the place that best describes how they feel The score for each item is automatically recorded and the overall score can range from 0 to 40 (sum score mean  =  9.59, SD  =  7.7); higher scores indicate more global subjective distress [22] Cronbach’s α in the

cur-rent sample was 0.81 (n = 7448)

Impact of health on daily life was measured with the

EQ 5D-Y [23] It has five dimensions: mobility (‘walking about’), self-care (‘looking after myself’), usual activities (‘doing usual activities’), pain and discomfort (‘having pain or discomfort’) and anxiety and depression (‘feeling worried, sad or unhappy’) All items refer to the health state ‘today’ Each item has three levels of problems reported (1 = no problems, 2 = some problems and 3 = a lot of problems) (sum score mean  =  6.20, SD  =  1.46)

Cronbach’s α in the current sample was 0.65 (n = 7038) Health today was also measured using the EQ 5D-Y It

included a visual analogue scale where the children rated their overall health status on a scale from 0 to 100 with

0 representing the worst and 100 representing the best health state they can imagine (on that day) In the cur-rent study, it was recoded so that higher scores indicated worse health (sum score mean = 20.64, SD = 19.8)

Special educational needs (SEN), eligibility for free school meals (FSM), and English as an additional lan-guage (EAL) were derived from the national pupil

data-base (NPD) SEN were data-based on the school’s assignment

of a child to a level of special educational needs Children with SEN, whether with or without statement, were con-sidered as having special educational needs FSM is fre-quently used as an indicator of low family income since only families on income support are entitled to claim free school meals Lastly, EAL was coded as present if a child’s first language was not English

Procedure

Ethical approval was obtained from the University Col-lege London Research Ethics Committee Children completed questionnaires using a secure online system during their usual school day with parent consent Before pupils responded to the survey, teachers read an infor-mation sheet to them which highlighted confidentiality

of their answers as well as their right to withdraw from the study Children provided informed consent prior to

completing the survey The online system was designed

to be easy to read and child friendly

Analyses

The structure and psychometric properties of the SRS

were investigated in several stages Firstly, confirmatory

factor analysis (CFA) was conducted, using Mplus

ver-sion 7.11 [24], to confirm whether constructs identified

as subscales in previous research of this measure are

evi-dent in the current sample This analysis was controlled

for intra-class correlation due to clustering by schools

[25] Secondly, differential item functioning (DIF) was

investigated across a range of demographic groupings

using the Mantel–Haenszel procedure and the Liu–

Agresti common log odds ratio as a measure of effect size

[26] in DIFAS 5.0 [27] Thirdly, DTF was conducted to

examine the measurement invariance directly at the scale

level across different subgroups in DIFAS 5.0 Fourthly,

Cronbach’s α and McDonald’s ω were calculated, using

SPSS version 21 and R, to assess the reliability of the

sub-scales Fifthly, to identify the association between

pro-tective factors and mental health outcomes, correlations

were run between the SRS subscales and mental health

outcomes using SPSS version 21 Lastly, to investigate

whether internal or external factors had an impact on

mental health outcomes, all subscales of the SRS were

entered into regression models at the same time

predict-ing each of the health outcomes Both unadjusted and

adjusted (adjusted for gender, school level—primary/

secondary—SEN, EAL and FSM) random effects linear

regression analyses (allowing for different school

inter-cepts) were run using STATA version 12; unstandardized

Bs, standard error and p-values are reported

Results

Factor structure

Confirmatory factor analysis for ordinal data with

weighted least squares with robust standard errors,

mean, and variance adjusted (WLSMV) estimator [28]

was carried out by testing a model with 10 correlated

factors indicated by previous research (Table 1) Given

the large sample size, Chi-square was not used to test

model fit [29] Other fit indices (CFI = 0.99; TLI = 0.99;

RMSEA  =  0.01, SRMR within  =  0.03; n  =  7663)

indi-cated a good model fit based on widely accepted criteria

[30] The correlation between 10 latent factors ranged

between 0.26 and 0.77 (Table 2)

Reliability

Since Cronbach’s α as a single measure for reliability is

no longer regarded as optimal [31], McDonald’s ω was

also used Coefficient ω gives a better estimate of

reli-ability than Cronbach’s α if the items of a scale are not

tau equivalent [32, 33] McDonald’s ωs were determined

using the factor loadings of the multilevel confirma-tory factor analysis (within-school factor models; only for the subscales with more than 2 items) The internal

consistency for all the subscales was good Cronbach’s α was 0.80 and McDonald’s ω was 0.89 (n = 7360) for the family connection subscale; α was 0.89 and ω was 0.91 (n = 7332) for the school connection subscale; α was 0.91 and ω was 0.94 (n = 7286) for the community connec-tion subscale; α was 0.79 and ω was 0.84 (n = 7288) for the participation in home and school life subscale; α was

0.74 (n = 7304) for the participation in community life

subscale; α was 0.80 and ω was 0.85 (n = 7358) for the self-esteem subscale; α was 0.77 (n = 7391) for the empa-thy subscale; α was 0.83 and ω was 0.87 (n = 7314) for the problem-solving subscale; α was 0.73 (n = 7324) for the goals and aspirations subscale; lastly α was 0.93 and ω

was 0.96 (n = 7052) for the peer support subscale

Differential item functioning (DIF) and differential test functioning (DTF)

In order to examine whether items behaved equivalently across a range of different subgroups of children, DIF analyses were undertaken for all subscales with more than two items The non-parametric Mantel–Haen-szel procedure was chosen to test for DIF since it is not based on the assumptions of a specific item response model [34] Nevertheless, the subscales were checked to

be sufficiently unidimensional based on a single factor multi-level CFA and which was acceptable according to standard criteria for all subscales and only mild violations for ‘participation in home and school life’ were found (see Additional file 1: Table S1 for details) Further, whether the CFA model’s thresholds were ordered along the latent continuum was inspected Higher item categories corre-sponded to higher trait levels and only the space on the latent trait corresponding to category 2 was for some items comparatively small [35] (see Additional file 2

Table S2 for item thresholds)

In the DIF analysis, six grouping criteria were exam-ined: gender, primary/secondary school level, whether the child had any elevated special educational need (SEN), whether English was the child’s second language (EAL), and whether the child was eligible for free school meals (FSM) Boys (42.3%, n = 2591), secondary school children (75.0%, n  =  4592), children with SEN with or without statement (18.6%, n = 950), non-native English speakers (17.6%, n = 1064), and children receiving FSM (18.4%, n = 1116) were the focus of these investigations (and formed the focal group in DIF analyses) DIF anal-yses compare the item endorsement rates in the focal group compared to reference group (e.g children with SEN with or without statement compared to all other

Table 1 CFA standardised loadings, measurement errors and intra-class correlations (by school)

Family connection At home, there is an adult who:

Is interested in my school work 0.77 0.007 0.040 Believes that I will be a success 0.85 0.006 0.034

Listens to me when I have something to say 0.81 0.005 0.037 School connection At school, there is an adult who:

Tells me when I do a good job 0.86 0.011 0.140 Listens to me when I have something to say 0.84 0.006 0.147 Believes that I will be a success 0.85 0.010 0.114 Community connection Away from school, there is an adult who:

Tells me when I do a good job 0.92 0.002 0.038 Believes that I will be a success 0.94 0.002 0.040

Participation in home

and school life Home and school I do things at home that make a difference

I help my family make decisions 0.75 0.005 0.014

At school, I decide things like class activities

I do things at my school that make a differ-ence (i.e make things better) 0.81 0.005 0.044 Participation in community life Away from school

I am a member of a club, sports team, church group, or other group 0.80 0.012 0.063

I take lessons in music, art, sports, or have

There are many things that I do well 0.83 0.005 0.064 Empathy I feel bad when someone gets their

I try to understand what other people feel 0.87 0.006 0.036 Problem solving When I need help, I find someone to talk to 0.83 0.004 0.043

I know where to go for help when I have a

I try to work out problems by talking about

Goals and aspirations I have goals and plans for future 0.75 0.007 0.039

I think I will be successful when I grow up 0.90 0.006 0.065 Peer support Are there students at your school who would:

Choose you on their team at school 0.72 0.005 0.029 Explain the rules of a game if you didn’t

Help you if you hurt yourself 0.84 0.005 0.050 Miss you if you weren’t at school 0.79 0.004 0.040 Make you feel better if something is

children), conditioning on test scores If children with

the same overall score on a subscale (e.g overall family

connection subscale score) have different probabilities

of endorsing an individual item then this item is said to

show differential item functioning; in other words the

item behaves differently across the two groups

The Liu-Agresti common logs ratio [L-A-LOR; 26] was

used to assess the size of potential DIF effects and to

gauge their potential relevance Positive L-A-LOR values

indicate the item is more difficult to endorse for the focal

group, while negative L-A-LOR values indicate that the

item is more difficult to endorse for the reference group,

given the same level of underlying trait The magnitude

of DIF was interpreted using a widely accepted

classify-ing system [36]: the magnitude was negligible if L-A-LOR

was less than 0.43, moderate if L-A-LOR was between

0.43 and 0.64, and large if L-A-LOR was greater than

0.64

According to the DIF analysis (Table 3), boys were

more likely to agree that students at school were more

likely to pick them for a partner and girls were more

likely to agree that students told them their secrets and

missed them if they weren’t at school Primary school

children were more likely to agree that they do things at

school which make a difference; that their peers explain

to them the rules if they don’t understand, and that their peers help them if they hurt themselves Secondary school children were more likely than primary school children to endorse that there are students at their school who would tell them their secrets Children with SEN were more likely to agree that their family listens to them when they have something to say Lastly, children without EAL were more likely to agree that peers invite them to their home Analyses did not indicate any DIF based FSM eligibility

Differential test functioning (DTF) assesses the aggre-gate effect of DIF across all the items in a scale [37] A scale with a DIF effect variance of ν2 below 0.07 can be classified as having small DTF, whereas DTF would be considered medium for 0.07  ≤  ν2  ≤  0.14 and large for

ν2 > 0.14 [37] All subscales, except the peer support scale, showed small DTF whereas the peer support sub-scale showed moderate DTF (Table 3)

The student resilience survey and mental health problems

A pattern was identified whereby the subscales of the SRS are negatively associated with mental health dif-ficulties, global subjective distress and impact on health (Table 4) In terms of negligible associations, empathy demonstrated very low correlations with both emotional

All factor loadings in CFA are significant at p < 0.001; CFI = 0.99; TLI = 0.99; RMSEA = 0.01, SRMR within = 0.03; SRMR between = 0.62; n = 7663

Table 1 continued

Help you if other students are being mean

Tell you you’re their friend 0.87 0.004 0.031 Ask you to join in when you are all alone 0.86 0.003 0.039

Table 2 Factor correlations

All factor correlations are significant at p < 0.0001 level CFI = 0.99; TLI = 0.99; RMSEA = 0.01, SRMR within = 0.03; SRMR between = 0.62; n = 7663

3 Community connection 0.74 0.56 1

4 Participation in home and school life 0.60 0.51 0.54 1

5 Participation in community life 0.30 0.26 0.31 0.42 1

G (focal 

ells me when I do a good job

ells me when I do a good job

ticipation in home and school lif

I do things at home that mak

G (focal 

I can do most things if I tr

Explain the rules of a game if y

G (focal 

2  < 0.07 indica

2  ≤ 0.14 indica

2  > 0.14 indica

problems and health On the other hand, both

self-esteem and problem solving had moderate correlations

with emotional problems and global subjective distress

Furthermore, in order to investigate whether internal

or external factors had an impact on mental health

out-comes, all subscales of the SRS were entered into linear

regression models (with school as a random effects term)

at the same time predicting each of the health outcomes

(Table 5) The adjusted regression results showed that

family connection, self-esteem, problem solving and peer

support were negatively associated with all the mental

health outcomes On the other hand, those who had high

empathy were more likely to display mental health

diffi-culties, global subjective distress and impact on health

Discussion

The aim of this study was to replicate the psychometric

properties of the student resilience survey (SRS) within

an English sample, to investigate the measurement

invar-iance in subgroups, and to investigate the relationship

between SRS subscales and mental health

On the basis of the confirmatory factor analysis, the

factor structure of the measure was similar to the original

validation study showing that 41 items loaded uniquely

onto their respective 10 subscales Moreover, similar to

the validation study, the analyses provided evidence for

acceptable reliability of all the subscales for this

sam-ple, especially considering that some of them are very

short//have very few items//something like that [10]

Nevertheless, the ‘participation in home and school life’ showed in individual analyses that it is not unidimen-sional and will need further research investigating its structure

This study extended previous research by generat-ing evidence of the SRS’s validity via analysis of DIF and DTF Our DIF analysis indicated differences between boys and girls in terms of how they answered the peer support subscale of the SRS This may be due to the dif-ferences between boys’ and girls’ expectations from friends One study has shown that boys describe friends

as “people you play with” whereas girls describe them

as “people you can trust” [38] Moreover, DIF analyses suggested that children in secondary school were more likely to agree that their peers shared secrets with them This is in line with literature suggesting that during mid-dle childhood, the quality of friendship changes from relationships characterised by the temporary sharing of activities and leisure time to friendships enhanced by intimacy, help, certainty, loyalty and confidence [39] Primary school children were also more likely to agree that their peers explained the rules of play to them and helped if they hurt themselves This may be due to chil-dren in secondary school becoming more independent and working through their problems more easily than younger children [40] Children with special educa-tional needs were more likely to agree that their families listened to them when they have something to say The quality of caregiving by parents is crucial for children

Table 4 Correlations between student resilience subscales and other scales

* p < 0.0001

Emotional problems Behavioural

problems Global subjective distress Impact of health on daily life Health today (0–100)

Family connection −.29*

(n = 6944) −.34*(n = 6995) −.41*(n = 7160) −.31*(n = 6775) −.30*(n = 7033) School connection −.22*

(n = 6911) −.26*(n = 6963) −.39*(n = 7134) −.21*(n = 6752) −.25*(n = 7013) Community connection −.29*

(n = 6876) −.25*(n = 6926) −.36*(n = 7082) −.27*(n = 6717) −.28*(n = 6967) Participation in home and

school life −.32*(n = 6903) −.29*(n = 6958) −.40*(n = 7089) −.26*(n = 6753) −.27*(n = 7002) Participation in community

life −.15*(n = 6882) −.10*(n = 6932) −.16*(n = 7101) −.11*(n = 6736) −.19*(n = 6987)

(n = 6969) −.32*(n = 7018) −.49*(n = 7157) −.35*(n = 6816) −.36*(n = 7072)

(n = 7004) −.27*(n = 7045) −.21*(n = 7189) −.10*(n = 6847) −.17*(n = 7105) Problem solving −.37*

(n = 6933) −.33*(n = 6980) −.44*(n = 7112) −.30*(n = 6778) −.29*(n = 7038) Goals and aspirations −.32*

(n = 6939) −.23*(n = 6980) −.37*(n = 7122) −.25*(n = 6787) −.28*(n = 7042)

(n = 6681) −.24*(n = 6725) −.35*(n = 6855) −.31*(n = 6533) −.27*(n = 6772)